PepBio: predicting the bioactivity of host defense peptides
| Autor: | Pornlada Nuchnoi, Watshara Shoombuatong, Saw Simeon, Thet Su Win, Jarl E. S. Wikberg, Aijaz Ahmad Malik, Theeraphon Piacham, Abdul Hafeez Kandhro, Hao Li, Chanin Nantasenamat, M. Paul Gleeson |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
General Chemical Engineering Decision tree External validation General Chemistry Computational biology Biology Matthews correlation coefficient Random forest Toxicology 03 medical and health sciences 030104 developmental biology Amino acid composition Evaluated data Host (network) |
| Zdroj: | RSC Advances. 7:35119-35134 |
| ISSN: | 2046-2069 |
| Popis: | Host defense peptides (HDPs) represents a class of ubiquitous and rapid responding immune molecules capable of direct inactivation of a wide range of pathogens. Recent research has shown HDPs to be promising candidates for development as a novel class of broad-spectrum chemotherapeutic agent that is effective against both pathogenic microbes and malignant neoplasm. This study aims to quantitatively explore the relationship between easy-to-interpret amino acid composition descriptors of HDPs with their respective bioactivities. Classification models were constructed using the C4.5 decision tree and random forest classifiers. Good predictive performance was achieved as deduced from the accuracy, sensitivity and specificity in excess of 90% and Matthews correlation coefficient in excess of 0.5 for all three evaluated data subsets (e.g. training, 10-fold cross-validation and external validation sets). The source code and data set used for the construction of classification models are available on GitHub at https://github.com/chaninn/pepbio/. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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